Date of Award
2023-05-01
Degree Name
Doctor of Philosophy
Department
Computational Science
Advisor(s)
Ori Rosen
Abstract
Elliptical copulas provide flexibility in modeling the dependence structure of a random vector. They are often parameterized with a correlation matrix and a scalar function, called generator. The estimation of the generator can be challenging, because it is a functional parameter. In this dissertation, we provide a rigorous approach to estimating the generator in a Bayesian framework, which is simpler, more robust, and outperforms existing estimation methods in the literature. Based on the proposed framework in this dissertation, other researchers may modify the model for other types of generators in their own research.
Language
en
Provenance
Recieved from ProQuest
Copyright Date
2023-05-01
File Size
p.
File Format
application/pdf
Rights Holder
Panfeng Liang
Recommended Citation
Liang, Panfeng, "Nonparametric Estimation of Elliptical Copulas" (2023). Open Access Theses & Dissertations. 3815.
https://scholarworks.utep.edu/open_etd/3815